| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, |
| # software distributed under the License is distributed on an |
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| # KIND, either express or implied. See the License for the |
| # specific language governing permissions and limitations |
| # under the License. |
| |
| import tvm |
| import tvm.testing |
| from tvm.script import ir as I, relax as R, tir as T |
| |
| |
| class BaseCompare(tvm.testing.CompareBeforeAfter): |
| transform = tvm.relax.transform.ComputePrimValue() |
| |
| |
| class TestPrimValueInAssertCondition(BaseCompare): |
| @I.ir_module |
| class Before: |
| @R.function(pure=False) |
| def main(A: R.Tensor(["N"])): |
| N = T.int64() |
| _ = R.assert_op(N % 16 == 0) |
| return A |
| |
| @I.ir_module |
| class Expected: |
| @R.function(pure=False) |
| def main(A: R.Tensor(["N"])): |
| N = T.int64() |
| condition: R.Prim("bool") = Expected.compute_symbolic_expr(R.prim_value(N)) |
| _ = R.assert_op(condition) |
| return A |
| |
| @T.prim_func(private=True) |
| def compute_symbolic_expr(N: T.int64) -> T.bool: |
| T.func_attr({"tir.is_host_func": True}) |
| T.ret(N % 16 == 0) |
| |
| |
| class TestPrimValueInBranchCondition(BaseCompare): |
| @I.ir_module |
| class Before: |
| @R.function(pure=False) |
| def main(A: R.Tensor(["N"])): |
| N = T.int64() |
| if R.prim_value(N % 16 == 0): |
| out = R.call_packed("fast_vectorized_impl", A, sinfo_args=[A.struct_info]) |
| else: |
| out = R.call_packed("slow_non_vectorized_impl", A, sinfo_args=[A.struct_info]) |
| return out |
| |
| @I.ir_module |
| class Expected: |
| @R.function(pure=False) |
| def main(A: R.Tensor(["N"])): |
| N = T.int64() |
| condition: R.Prim("bool") = Expected.compute_symbolic_expr(R.prim_value(N)) |
| if condition: |
| out = R.call_packed("fast_vectorized_impl", A, sinfo_args=[A.struct_info]) |
| else: |
| out = R.call_packed("slow_non_vectorized_impl", A, sinfo_args=[A.struct_info]) |
| return out |
| |
| @T.prim_func(private=True) |
| def compute_symbolic_expr(N: T.int64) -> T.bool: |
| T.func_attr({"tir.is_host_func": True}) |
| T.ret(N % 16 == 0) |
| |
| |
| class TestPrimValueInPureFunction(BaseCompare): |
| @I.ir_module |
| class Before: |
| @R.function |
| def main(_N: R.Prim(value="N"), _M: R.Prim(value="M")) -> R.Prim(value="N*M"): |
| N = T.int64() |
| M = T.int64() |
| out = R.prim_value(N * M) |
| return out |
| |
| @I.ir_module |
| class Expected: |
| @R.function |
| def main(_N: R.Prim(value="N"), _M: R.Prim(value="M")) -> R.Prim(value="N*M"): |
| N = T.int64() |
| M = T.int64() |
| out = Expected.compute_symbolic_expr(R.prim_value(N), R.prim_value(M)) |
| return out |
| |
| @T.prim_func(private=True) |
| def compute_symbolic_expr(N: T.int64, M: T.int64) -> T.int64: |
| T.func_attr({"tir.is_host_func": True}) |
| T.ret(N * M) |
| |
| |
| if __name__ == "__main__": |
| tvm.testing.main() |